Data gathering to enable the optimization of distributed wi-fi networks

ABSTRACT

Systems and methods for gathering data by an access point in a Wi-Fi system for optimization include periodically or based on command from a cloud-based system performing one or more of i) obtaining on-channel scanning data while operating on a home channel and ii) switching off the home channel and obtaining off-channel scanning data for one or more off-channels; and providing measurement data based on one or more of the on-channel scanning data and the off-channel scanning data to the cloud-based system for use in the optimization of the Wi-Fi system, wherein the measurement data comprises one or more of raw data and processed data.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present patent/application claims priority to U.S. ProvisionalPatent Application No. 62/310,589, filed Mar. 18, 2016, and entitled“DATA GATHERING TO ENABLE THE OPTIMIZATION OF DISTRIBUTED WI-FINETWORKS,” the contents of which are incorporated by reference.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to wireless networking systemsand methods. More particularly, the present disclosure relates to datagathering to enable the optimization of distributed Wi-Fi networks.

BACKGROUND OF THE DISCLOSURE

Wi-Fi networks (i.e., Wireless Local Area Networks (WLAN) based on theIEEE 802.11 standards) have become ubiquitous. People use them in theirhomes, at work, and in public spaces such as schools, cafes, even parks.Wi-Fi provides great convenience by eliminating wires and allowing formobility. The applications that consumers run over Wi-Fi is continuallyexpanding. Today people use Wi-Fi to carry all sorts of media, includingvideo traffic, audio traffic, telephone calls, video conferencing,online gaming, and security camera video. Often traditional dataservices are also simultaneously in use, such as web browsing, fileupload/download, disk drive backups, and any number of mobile deviceapplications. In fact, Wi-Fi has become the primary connection betweenuser devices and the Internet in the home or other locations. The vastmajority of connected devices use Wi-Fi for their primary networkconnectivity.

Despite Wi-Fi's popularity and ubiquity, many consumers still experiencedifficulties with Wi-Fi. The challenges of supplying real-time mediaapplications, like those listed above, put increasing demands on thethroughput, latency, jitter, and robustness of Wi-Fi. Studies have shownthat broadband access to the Internet through service providers is up99.9% of the time at high data rates. However, despite the Internetarriving reliably and fast to the edge of consumer's homes, simplydistributing the connection across the home via Wi-Fi is much lessreliable leading to poor user experience.

Several issues prevent conventional Wi-Fi systems from performing well,including i) interference, ii) congestion, and iii) coverage. Forinterference, with the growth of Wi-Fi has come the growth ofinterference between different Wi-Fi networks which overlap. When twonetworks within range of each other carry high levels of traffic, theyinterfere with each other, reducing the throughput that either networkcan achieve. For congestion, within a single Wi-Fi network, there may beseveral communications sessions running. When several demandingapplications are running, such as high definition video streams, thenetwork can become saturated, leaving insufficient capacity to supportthe video streams.

For coverage, Wi-Fi signals attenuate with distance and when travelingthrough walls and other objects. In many environments, such asresidences, reliable Wi-Fi service cannot be obtained in all rooms. Evenif a basic connection can be obtained in all rooms, many of thoselocations will have poor performance due to a weak Wi-Fi signal. Variousobjects in a residence such as walls, doors, mirrors, people, andgeneral clutter all interfere and attenuate Wi-Fi signals leading toslower data rates.

Two general approaches have been tried to improve the performance ofconventional Wi-Fi systems. The first approach is to simply build morepowerful single access points, in an attempt to cover a location withstronger signal strengths, thereby providing more complete coverage andhigher data rates at a given location. However, this approach is limitedby both regulatory limits on the allowed transmit power, and by thefundamental laws of nature. The difficulty of making such a powerfulaccess point, whether by increasing the power, or increasing the numberof transmit and receive antennas, grows exponentially with the achievedimprovement. Practical improvements using these techniques lie in therange of 6 to 12 dB. However, a single additional wall can attenuate by12 dB. Therefore, despite the huge difficulty and expense to gain 12 dBof link budget, the resulting system may not be able to transmit througheven one additional wall. Any coverage holes that may have existed willstill be present, devices that suffer poor throughput will still achieverelatively poor throughput, and the overall system capacity will be onlymodestly improved. In addition, this approach does nothing to improvethe situation with interference and congestion. In fact, by increasingthe transmit power, the amount of interference between networks actuallygoes up.

A second approach is to use repeaters or a mesh of Wi-Fi devices torepeat the Wi-Fi data throughout a location. This approach is afundamentally better approach to achieving better coverage. By placingeven a single repeater node in the center of a house, the distance thata single Wi-Fi transmission must traverse can be cut in half, halvingalso the number of walls that each hop of the Wi-Fi signal musttraverse. This can make a change in the link budget of 40 dB or more, ahuge change compared to the 6 to 12 dB type improvements that can beobtained by enhancing a single access point as described above. Meshnetworks have similar properties as systems using Wi-Fi repeaters. Afully interconnected mesh adds the ability for all the repeaters to beable to communicate with each other, opening the possibility of packetsbeing delivered via multiple hops following an arbitrary pathway throughthe network.

State of the art mesh or repeaters systems still have many limitations.Because the systems depend on localized control, they configurethemselves to use the same frequency for all the backhaul communicationbetween the repeaters or mesh nodes. This creates a severe systemcapacity problem. Consider a system that requires three hops through thenetwork to get its packet to the destination. Since all three hops areon the same frequency channel, and because only one Wi-Fi radio cantransmit at a time on a given channel among devices that are in range(where range is determined by the long range of the lowest supporteddata rate), only one hop can be active at a time. Therefore, for thisexample, delivering a packet via three hops would consume three timesthe airtime on the one channel as delivering the packet directly. In thefirst hop, when the packet is moving from the Wi-Fi gateway to the firstmesh node, all the other links in the house would need to stay silent.Similarly, as the packet is later sent from the first mesh node to asecond mesh node, no other Wi-Fi devices in the home could transmit.Finally, the same would be true as the packet is moved from the secondmesh node to the final destination. In all, the use of three hoprepeating has reduced the network capacity by a factor of three. And, aswith the case of a single access point, the repeater or mesh approachdoes nothing to help with the problems of interference or congestion. Asbefore, the technique actually increases interference, as a singlepacket transmission becomes three separate transmissions, taking a totalof 3× the airtime, generating 3× the interference to neighboring Wi-Finetworks.

BRIEF SUMMARY OF THE DISCLOSURE

In an exemplary embodiment, a method for gathering data by an accesspoint in a Wi-Fi system for optimization includes periodically or basedon command from a cloud-based system performing one or more of i)obtaining on-channel scanning data while operating on a home channel andii) switching off the home channel and obtaining off-channel scanningdata for one or more off-channels; and providing measurement data basedon one or more of the on-channel scanning data and the off-channelscanning data to the cloud-based system for use in the optimization ofthe Wi-Fi system, wherein the measurement data includes one or more ofraw data and processed data. When the measurement data is the processeddata, the method can further include determining a division of time thatthe home channel is divided based on a combination of directmeasurements and computations based on the direct measurements. Whenmeasurement data is the processed data, the method can further includedetermining delays of packets over the Wi-Fi system through one ofdirect measurements and statistics of delay at the access point. Themeasurement data can include a plurality of Received Signal StrengthIndicators (RSSI), achievable data rates, capacity, load, error rates,delays, interference, and fractions of time spent transmitting andreceiving.

The switching off can be determined based on a load of the access point.The method can further include notifying other access points in theWi-Fi system of the switching off. The access point can be lightlyloaded compared to at least one additional access point in the Wi-Fisystem, and wherein the access point can be configured to perform theobtaining off-channel scanning data for the at least one additionalaccess point. The access point for the off-channel scanning data cansend probe requests for a particular Service Set Identifier (SSID) tomeasure signal strengths to a particular neighbor to reduce the numberof probe responses received. The access point for the off-channelscanning data can send frames that spoof another in-network BasicService Set Identifier (BSSID) to elicit responses from neighboringaccess points and clients. The method can further include receivingconfiguration data from the cloud-based system based on theoptimization, wherein the providing measurement data is performed over astatistics channel, and the receiving configuration data is performedover a configuration channel different from the statistics channel. Theone or more of the on-channel scanning data and the off-channel scanningdata can be obtained at different channel bandwidths. The method canfurther include causing Wi-Fi client devices to move to other accesspoints prior to the switching off.

In a further exemplary embodiment, an access point in a Wi-Fi systemconfigured to gather data for optimization includes a plurality ofradios communicating on the Wi-Fi system; and a processorcommunicatively coupled to the plurality of radios and configured to:periodically or based on command from a cloud-based system cause the oneor more radios to one or more of i) obtain on-channel scanning datawhile operating on a home channel and ii) switch off the home channeland obtain off-channel scanning data for one or more off-channels; andprovide measurement data based on the on-channel scanning data and theoff-channel scanning data to the cloud-based system for use in theoptimization of the distributed Wi-Fi system, wherein the measurementdata includes one or more of raw data and processed data. The processorcan be further configured to notify other access points in the Wi-Fisystem of the switching off. The access point can be lightly loadedcompared to at least one additional access point in the Wi-Fi system,and wherein the access point can be configured to obtain the off-channelscanning data for the at least one additional access point. The accesspoint for the off-channel scanning data can send probe requests for aparticular Service Set Identifier (SSID) to measure signal strengths toa particular neighbor to reduce the number of probe responses received.The access point for the off-channel scanning data can send frames thatspoof another in-network Basic Service Set Identifier (BSSID) to elicitresponses from neighboring access points and clients. The processor canbe further configured to receive configuration data from the cloud-basedsystem based on the optimization, wherein the measurement data isprovided over a statistics channel, and the receiving configuration datais provided over a configuration channel different from the statisticschannel. The processor can be further configured cause Wi-Fi clientdevices to move to other access points prior to the switching off.

In a further exemplary embodiment, a cloud-based system configured toobtain data from a Wi-Fi system for optimization includes a networkinterface communicatively coupled to the Wi-Fi system including aplurality of access points communicatively coupled to one another and atleast one access point communicatively coupled to a gateway providingexternal communication to the cloud-based system; one or more processorscommunicatively coupled to the network interface; and memory storinginstructions that, when executed, cause the one or more processors toreceive periodically or based on command from the cloud-based system oneor more of i) on-channel scanning data while an access point isoperating on a home channel and ii) off-channel scanning data for one ormore off-channels where the access point switches off the home channel;and analyze measurement data based on the on-channel scanning data andthe off-channel scanning data for use in the optimization of the Wi-Fisystem, wherein the measurement data includes one or more of raw dataand processed data, and wherein the measurement data is analyzed fromthe plurality of access points.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated and described herein withreference to the various drawings, in which like reference numbers areused to denote like system components/method steps, as appropriate, andin which:

FIG. 1 is a network diagram of a distributed Wi-Fi system withcloud-based control;

FIG. 2 is a network diagram of differences in operation of thedistributed Wi-Fi system of FIG. 1 relative to a conventional singleaccess point system, a Wi-Fi mesh network, and a Wi-Fi repeater system;

FIG. 3 is a flowchart of a configuration and optimization process forthe distributed Wi-Fi system of FIG. 1;

FIG. 4 is a block diagram of inputs and outputs to an optimization aspart of the configuration and optimization process of FIG. 3;

FIG. 5 is a block diagram of functional components of the access pointin the distributed Wi-Fi system of FIG. 1;

FIG. 6 is a block diagram of functional components of a server, a Wi-Ficlient device, or a user device which may be used with the distributedWi-Fi system of FIG. 1;

FIG. 7 is a flowchart of a data gathering process by an access point inthe distributed Wi-Fi system of FIG. 1;

FIG. 8 is a graph of fractions of time-related to a channel, such as thehome channel; and

FIG. 9 is a network diagram of a network with an access point in thedistributed Wi-Fi system of FIG. 1 communicating with a configurationservice and a statistics service in the cloud.

DETAILED DESCRIPTION OF THE DISCLOSURE

Again, in various exemplary embodiments, the present disclosure relatesto data gathering systems and methods to enable the optimization ofdistributed Wi-Fi networks. It is an objective of the systems andmethods to provide a Wi-Fi network with superior performance relative toWi-Fi networks with a single AP, with repeaters, or with multiple meshnodes. The systems and methods include a distributed Wi-Fi system with aplurality of access points (nodes) which are self-optimizing based oncloud-based control. This self-optimization adapts the topology andconfiguration of the plurality of access points in real-time based onthe operating environment. The plurality of access points communicatewith one another via backhaul links and to Wi-Fi client devices viaclient links, and the each of the backhaul links and each of the clientlinks may use different channels based on the optimization, therebyavoiding the aforementioned limitations in Wi-Fi mesh or repeatersystems. In an exemplary aspect, the distributed Wi-Fi system includes arelatively large number of access points (relative to conventionaldeployments including Wi-Fi mesh or repeater systems). For example, thelarge number of access points can be 6 to 12 or more in a typicalresidence. With a large number of access points, the distance betweenany two access points is small, on a similar scale as the distancebetween an access point and Wi-Fi client device. Accordingly, signalstrength is maintained avoiding coverage issues, and with theoptimization of the topology and configuration, congestion andinterference are minimized. Thus, the distributed Wi-Fi system addressesall three of the aforementioned limitations in conventional Wi-Fisystems.

Distributed Wi-Fi System

Referring to FIG. 1, in an exemplary embodiment, a network diagramillustrates a distributed Wi-Fi system 10 with cloud-based 12 control.The distributed Wi-Fi system 10 can operate in accordance with the IEEE802.11 protocols and variations thereof. The distributed Wi-Fi system 10includes a plurality of access points 14 (labeled as access points14A-14H) which can be distributed throughout a location, such as aresidence, office, or the like. That is, the distributed Wi-Fi system 10contemplates operation in any physical location where it is inefficientor impractical to service with a single access point, repeaters, or amesh system. As described herein, the distributed Wi-Fi system 10 can bereferred to as a network, a system, a Wi-Fi network, a Wi-Fi system, acloud-based system, etc. The access points 14 can be referred to asnodes, access points, Wi-Fi nodes, Wi-Fi access points, etc. Theobjective of the access points 14 is to provide network connectivity toWi-Fi client devices 16 (labeled as Wi-Fi client devices 16A-16E). TheWi-Fi client devices 16 can be referred to as client devices, userdevices, clients, Wi-Fi clients, Wi-Fi devices, etc.

In a typical residential deployment, the distributed Wi-Fi system 10 caninclude between 3 to 12 access points or more in a home. A large numberof access points 14 (which can also be referred to as nodes in thedistributed Wi-Fi system 10) ensures that the distance between anyaccess point 14 is always small, as is the distance to any Wi-Fi clientdevice 16 needing Wi-Fi service. That is, an objective of thedistributed Wi-Fi system 10 is for distances between the access points14 to be of similar size as distances between the Wi-Fi client devices16 and the associated access point 14. Such small distances ensure thatevery corner of a consumer's home is well covered by Wi-Fi signals. Italso ensures that any given hop in the distributed Wi-Fi system 10 isshort and goes through few walls. This results in very strong signalstrengths for each hop in the distributed Wi-Fi system 10, allowing theuse of high data rates, and providing robust operation. Note, thoseskilled in the art will recognize the Wi-Fi client devices 16 can bemobile devices, tablets, computers, consumer electronics, homeentertainment devices, televisions, or any network-enabled device. Forexternal network connectivity, one or more of the access points 14 canbe connected to a modem/router 18 which can be a cable modem, DigitalSubscriber Loop (DSL) modem, or any device providing external networkconnectivity to the physical location associated with the distributedWi-Fi system 10.

While providing excellent coverage, a large number of access points 14(nodes) presents a coordination problem. Getting all the access points14 configured correctly and communicating efficiently requirescentralized control. This control is preferably done on servers 20 thatcan be reached across the Internet (the cloud 12) and accessed remotelysuch as through an application (“app”) running on a user device 22. Therunning of the distributed Wi-Fi system 10, therefore, becomes what iscommonly known as a “cloud service.” The servers 20 are configured toreceive measurement data, to analyze the measurement data, and toconfigure the access points 14 in the distributed Wi-Fi system 10 basedthereon, through the cloud 12. The servers 20 can also be configured todetermine which access point 14 each of the Wi-Fi client devices 16connect (associate) with. That is, in an exemplary aspect, thedistributed Wi-Fi system 10 includes cloud-based control (with acloud-based controller or cloud service) to optimize, configure, andmonitor the operation of the access points 14 and the Wi-Fi clientdevices 16. This cloud-based control is contrasted with a conventionaloperation which relies on local configuration such as by logging inlocally to an access point. In the distributed Wi-Fi system 10, thecontrol and optimization does not require local login to the accesspoint 14, but rather the user device 22 (or a local Wi-Fi client device16) communicating with the servers 20 in the cloud 12, such as via adisparate network (a different network than the distributed Wi-Fi system10) (e.g., LTE, another Wi-Fi network, etc.).

The access points 14 can include both wireless links and wired links forconnectivity. In the example of FIG. 1, the access point 14A has anexemplary gigabit Ethernet (GbE) wired connection to the modem/router18. Optionally, the access point 14B also has a wired connection to themodem/router 18, such as for redundancy or load balancing. Also, theaccess points 14A, 14B can have a wireless connection to themodem/router 18. The access points 14 can have wireless links for clientconnectivity (referred to as a client link) and for backhaul (referredto as a backhaul link). The distributed Wi-Fi system 10 differs from aconventional Wi-Fi mesh network in that the client links and thebackhaul links do not necessarily share the same Wi-Fi channel, therebyreducing interference. That is, the access points 14 can support atleast two Wi-Fi wireless channels—which can be used flexibly to serveeither the client link or the backhaul link and may have at least onewired port for connectivity to the modem/router 18, or for connection toother devices. In the distributed Wi-Fi system 10, only a small subsetof the access points 14 require direct connectivity to the modem/router18 with the non-connected access points 14 communicating with themodem/router 18 through the backhaul links back to the connected accesspoints 14.

Distributed Wi-Fi System Compared to Conventional Wi-Fi Systems

Referring to FIG. 2, in an exemplary embodiment, a network diagramillustrates differences in operation of the distributed Wi-Fi system 10relative to a conventional single access point system 30, a Wi-Fi meshnetwork 32, and a Wi-Fi repeater network 33. The single access pointsystem 30 relies on a single, high-powered access point 34 which may becentrally located to serve all Wi-Fi client devices 16 in a location(e.g., house). Again, as described herein, in a typical residence, thesingle access point system 30 can have several walls, floors, etc.between the access point 34 and the Wi-Fi client devices 16. Plus, thesingle access point system 30 operates on a single channel, leading topotential interference from neighboring systems. The Wi-Fi mesh network32 solves some of the issues with the single access point system 30 byhaving multiple mesh nodes 36 which distribute the Wi-Fi coverage.Specifically, the Wi-Fi mesh network 32 operates based on the mesh nodes36 being fully interconnected with one another, sharing a channel suchas a channel X between each of the mesh nodes 36 and the Wi-Fi clientdevice 16. That is, the Wi-Fi mesh network 32 is a fully interconnectedgrid, sharing the same channel, and allowing multiple different pathsbetween the mesh nodes 36 and the Wi-Fi client device 16. However, sincethe Wi-Fi mesh network 32 uses the same backhaul channel, every hopbetween source points divides the network capacity by the number of hopstaken to deliver the data. For example, if it takes three hops to streama video to a Wi-Fi client device 16, the Wi-Fi mesh network 32 is leftwith only ⅓ the capacity. The Wi-Fi repeater network 33 includes theaccess point 34 coupled wirelessly to a Wi-Fi repeater 38. The Wi-Firepeater network 33 is a star topology where there is at most one Wi-Firepeater 38 between the access point 14 and the Wi-Fi client device 16.From a channel perspective, the access point 34 can communicate to theWi-Fi repeater 38 on a first channel, Ch. X, and the Wi-Fi repeater 38can communicate to the Wi-Fi client device 16 on a second channel, Ch.Y.

The distributed Wi-Fi system 10 solves the problem with the Wi-Fi meshnetwork 32 of requiring the same channel for all connections by using adifferent channel or band for the various hops (note, some hops may usethe same channel/band, but it is not required), to prevent slowing downthe Wi-Fi speed. For example, the distributed Wi-Fi system 10 can usedifferent channels/bands between access points 14 and between the Wi-Ficlient device 16 (e.g., Chs. X, Y, Z, A), and, also, the distributedWi-Fi system 10 does not necessarily use every access point 14, based onconfiguration and optimization by the cloud 12. The distributed Wi-Fisystem 10 solves the problems of the single access point system 30 byproviding multiple access points 14. The distributed Wi-Fi system 10 isnot constrained to a star topology as in the Wi-Fi repeater network 33which at most allows two wireless hops between the Wi-Fi client device16 and a gateway. Also, the distributed Wi-Fi system 10 forms a treetopology where there is one path between the Wi-Fi client device 16 andthe gateway, but which allows for multiple wireless hops unlike theWi-Fi repeater network 33.

Wi-Fi is a shared, simplex protocol meaning only one conversationbetween two devices can occur in the network at any given time, and ifone device is talking the others need to be listening. By usingdifferent Wi-Fi channels, multiple simultaneous conversations can happensimultaneously in the distributed Wi-Fi system 10. By selectingdifferent Wi-Fi channels between the access points 14, interference andcongestion are avoided. The server 20 through the cloud 12 automaticallyconfigures the access points 14 in an optimized channel hop solution.The distributed Wi-Fi system 10 can choose routes and channels tosupport the ever-changing needs of consumers and their Wi-Fi clientdevices 16. The distributed Wi-Fi system 10 approach is to ensure Wi-Fisignals do not need to travel far-either for backhaul or clientconnectivity. Accordingly, the Wi-Fi signals remain strong and avoidinterference by communicating on the same channel as in the Wi-Fi meshnetwork 32 or with Wi-Fi repeaters. In an exemplary aspect, the servers20 in the cloud 12 are configured to optimize channel selection for thebest user experience.

Configuration and Optimization Process for the Distributed Wi-Fi System

Referring to FIG. 3, in an exemplary embodiment, a flowchart illustratesa configuration and optimization process 50 for the distributed Wi-Fisystem 10. Specifically, the configuration and optimization process 50includes various steps 51-58 to enable efficient operation of thedistributed Wi-Fi system 10. These steps 51-58 may be performed in adifferent order and may be repeated on an ongoing basis, allowing thedistributed Wi-Fi system 10 to adapt to changing conditions. First, eachof the access points 14 are plugged in and onboarded (step 51). In thedistributed Wi-Fi system 10, only a subset of the access points 14 arewired to the modem/router 18 (or optionally with a wireless connectionto the modem/router 18), and those access points 14 without wiredconnectivity have to be onboarded to connect to the cloud 12. Theonboarding step 51 ensures a newly installed access point 14 connects tothe distributed Wi-Fi system 10 so that the access point can receivecommands and provide data to the servers 20. The onboarding step 51 caninclude configuring the access point with the correct Service SetIdentifier (SSID) (network ID) and associated security keys. In anexemplary embodiment, the onboarding step 51 is performed with Bluetoothor equivalent connectivity between the access point 14 and a user device22 allowing a user to provide the SSID, security keys, etc. Onceonboarded, the access point 14 can initiate communication over thedistributed Wi-Fi system 10 to the servers 20 for configuration.

Second, the access points 14 obtain measurements and gather informationto enable optimization of the networking settings (step 52). Theinformation gathered can include signal strengths and supportable datarates between all nodes as well as between all nodes and all Wi-Ficlient devices 16. Specifically, the measurement step 52 is performed byeach access point 14 to gather data. Various additional measurements canbe performed such as measuring an amount of interference, loads(throughputs) required by different applications operating over thedistributed Wi-Fi system 10, etc. Third, the measurements and gatheredinformation from the measurement step 52 is provided to the servers 20in the cloud 12 (step 53). The steps 51-53 are performed on location atthe distributed Wi-Fi system 10.

These measurements in steps 52, 53 could include traffic load requiredby each client, the data rate that can be maintained between each of thenodes and from each of the nodes to each of the clients, the packeterror rates in the links between the nodes and between the nodes and theclients, and the like. In addition, the nodes make measurements of theinterference levels affecting the network. This includes interferencefrom other cloud controlled distributed Wi-Fi systems (“in-networkinterferers”), and interference coming from devices that are not part ofthe controllable network (“out-of-network interferers). It is importantto make a distinction between these types of interferers. In-networkinterferers can be controlled by the cloud system, and therefore can beincluded in a large optimization over all in-network systems. Out ofnetwork interferers cannot be controlled from the cloud, and thereforetheir interference cannot be moved to another channel or otherwisechanged. The system must adapt to them, rather than changing them. Theseout-of-network interferers include Wi-Fi networks that are not cloudcontrolled and non-Wi-Fi devices that transmit in the frequencies usedby Wi-Fi such as Bluetooth devices, baby monitors, cordless phones, etc.

Another important input is the delay of packets traversing the network.These delays could be derived from direct measurements, time stampingpackets as they arrive into the Wi-Fi network at the gateway, andmeasuring the elapsed time as they depart at the final node. However,such measurement would require some degree of time synchronizationbetween the nodes. Another approach would be to measure the statisticsof delay going through each node individually. The average total delaythrough the network and the distribution of the delays given someassumptions could then be calculated based on the delay statisticsthrough each node individually. Delay can then become a parameter to beminimized in the optimization. It is also useful for the optimization toknow the time that each node spends transmitting and receiving. Togetherwith the amount of information transmitted or received, this can be usedto determine the average data rate the various links are sustaining.

Fourth, the servers 20 in the cloud 12 use the measurements to performan optimization algorithm for the distributed Wi-Fi system 10 (step 54).The optimization algorithm outputs the best parameters for the networkoperation. These include the selection of the channels on which eachnode should operate for the client links and the backhaul links, thebandwidth on each of these channels that the node should use, thetopology of connection between the nodes and the routes for packetsthrough that topology from any source to any destination in the network,the appropriate node for each client to attach to, the band on whicheach client should attach, etc.

Specifically, the optimization uses the measurements from the nodes asinputs to an objective function which is maximized. A capacity for eachlink can be derived by examining the amount of data that has been moved(the load), and the amount of time that the medium is busy due tointerference. This can also be derived by taking a ratio of the datamoved across the link to the fraction of the time that the transmittingqueue was busy. This capacity represents the hypothetical throughputthat could be achieved if the link was loaded to saturation and wasmoving as much data as it possibly could.

Fifth, an output of the optimization is used to configure thedistributed Wi-Fi system 10 (step 55). The nodes and client devices needto be configured from the cloud based on the output of the optimization.Specific techniques are used to make the configuration fast, and tominimize the disruption to a network that is already operating. Theoutputs of the optimization are the operational parameters for thedistributed Wi-Fi system 10. This includes the frequency channels onwhich each of the nodes is operating, and the bandwidth of the channelto be used. The 802.11ac standard allows for channel bandwidths of 20,40, 80, and 160 MHz. The selection of the bandwidth to use is a tradeoffbetween supporting higher data rates (wide channel bandwidth), andhaving a larger number of different non-interfering channels to use inthe distributed Wi-Fi system 10. The optimization tries to use thelowest possible channel bandwidth for each link that will support theload required by the various user's applications. By using the narrowestsufficient throughput channels, the maximum number of non-interferingchannels are left over for other links within the distributed Wi-Fisystem 10.

The optimization generates the outputs from the inputs as describedabove by maximizing an objective function. There are many differentpossible objective functions. One objective could be to maximize thetotal throughput provided to all the clients. This goal has thedisadvantage that the maximum total throughput might be achieved bystarving some clients completely, in order to improve the performance ofclients that are already doing well. Another objective could be toenhance as much as possible the performance of the client in the networkin the worst situation (maximize the minimum throughput to a client).This goal helps promote fairness but might trade a very large amount oftotal capacity for an incremental improvement at the worst client. Apreferred approach considers the load desired by each client in anetwork, and maximizing the excess capacity for that load ratio. Theoptimization can improve the capacity, as well as shift the capacitybetween the two APs. The desired optimization is the one that maximizesthe excess capacity in the direction of the ratio of the loads. Thisrepresents giving the distributed Wi-Fi system 10 the most margin tocarry the desired loads, making their performance more robust, lowerlatency, and lower jitter. This strict optimization can be furtherenhanced by providing a softer optimization function that weighsassigning capacities with a varying scale. A high utility value would beplaced on getting the throughput to be higher than the required load.Providing throughput to a client or node above the required load wouldstill be considered a benefit, but would be weighted much less heavilythan getting all the clients/nodes to the load they are requiring. Sucha soft weighted optimization function allows for a more beneficialtradeoff of excess performance between devices.

Another set of optimization outputs defines the topology of thedistributed Wi-Fi system 10, meaning which nodes connect to which othernodes. The actual route through the distributed Wi-Fi system 10 betweentwo clients or the client and the Internet gateway (modem/router 18) isalso an output of the optimization. Again, the optimization attempts tochoose the best tradeoff in the route. Generally, traversing more hopsmakes each hop shorter range, higher data rate, and more robust.However, more hops add more latency, more jitter, and depending on thechannel frequency assignments, takes more capacity away from the rest ofthe system.

Sixth, learning algorithms can be applied to cloud-stored data fordetermining trends and patterns (step 56). Note, the servers 20 canstore the measurements from the nodes, results from the optimizations,and subsequent measurements after associated optimizations. With thisdata, trends and patterns can be determined and analyzed for variouspurposes. Because reconfiguring a network takes time and is always atleast partially disruptive to active communication, it is beneficial toconfigure the network for peak load, before that peak load arrives. Bylearning from the historical data that has already been captured, it ispossible to predict the usage and interference that will occur at afuture time. Other uses of learning on the captured data includeidentifying bugs and discovering bugs in the behavior of client devices.Once bugs in the behavior of client devices are discovered, it may bepossible to work around those bugs using tools and commands from theinfrastructure side of the network.

Seventh, the performance of the network can be assessed and reported tothe user or to a service provider whose services are running over Wi-Fi(step 57). Eighth, an application (such as a mobile app operating on theuser device 22) can provide a user visibility into the network operation(step 58). This would include the display of network activity andperformance metrics. The mobile app can be used to convey information tothe user, make measurements, and allow the user to control certainaspects of Wi-Fi the network operation. The mobile app also communicatesto the internet over the cellular system to assist in onboarding thenodes when they are first being set up. The mobile phone app, utilizingthe cellular system, also provides a way for the Wi-Fi network tocommunicate with the internet and cloud when the user's normal internetconnection is not functioning. This cellular based connection can beused to signal status, notify the service provider and other users, andcan even be used to carry data from the home to the internet during thetime that the user's normal internet connection is malfunctioning.

The configuration and optimization process 50 is described herein withreference to the distributed Wi-Fi system 10 as an exemplary embodiment.Those skilled in the art will recognize the configuration andoptimization process 50 can operate with any type of multiple node Wi-Fisystem (i.e., a distributed Wi-Fi network or Wi-Fi system) including theWi-Fi mesh network 32, the Wi-Fi repeater network 33, etc. For example,cloud-based control can also be implemented in the Wi-Fi mesh network32, the Wi-Fi repeater network 33, etc. and the various systems andmethods described herein can operate as well here for cloud-basedcontrol and optimization. Also, the terminology “distributed Wi-Finetwork” or “Wi-Fi system” can also apply to the Wi-Fi mesh network 32,the Wi-Fi repeater network 33, etc. whereas the distributed Wi-Fi system10 is a specific embodiment of a distributed Wi-Fi network. That is thedistributed Wi-Fi system 10 is similar to the Wi-Fi mesh network 32, theWi-Fi repeater network 33, etc. in that it does support multiple nodes,but it does have the aforementioned distinctions to overcome limitationsassociated with each.

Optimization

Referring to FIG. 3, in an exemplary embodiment, a block diagramillustrates inputs 60 and outputs 62 to an optimization 70. The inputs60 can include, for example, traffic load required by each client,signal strengths between nodes and between access points 14 (nodes) andWi-fi client devices 16, data rate for each possible link in thenetwork, packet error rates on each link, strength and load onin-network interferers, and strength and load on out-of-networkinterferers. Again, these inputs are based on measurements and datagathered by the plurality of access points 14 and communicated to theservers 20 in the cloud 12. The servers 20 are configured to implementthe optimization 70. The outputs of the optimization 70 include, forexample, channel and bandwidth (BW) selection, routes and topology,Request to Send/Clear to Send (RTS/CTS) settings, Transmitter (TX)power, clear channel assessment thresholds, client association steering,and band steering.

Access Point

Referring to FIG. 5, in an exemplary embodiment, a block diagramillustrates functional components of the access point 14 in thedistributed Wi-Fi system 10. The access point 14 includes a physicalform factor 100 which contains a processor 102, a plurality of radios104, a local interface 106, a data store 108, a network interface 110,and power 112. It should be appreciated by those of ordinary skill inthe art that FIG. 5 depicts the access point 14 in an oversimplifiedmanner, and a practical embodiment may include additional components andsuitably configured processing logic to support features describedherein or known or conventional operating features that are notdescribed in detail herein.

In an exemplary embodiment, the form factor 100 is a compact physicalimplementation where the access point 14 directly plugs into anelectrical socket and is physically supported by the electrical plugconnection to the electrical socket. This compact physicalimplementation is ideal for a large number of access points 14distributed throughout a residence. The processor 102 is a hardwaredevice for executing software instructions. The processor 102 can be anycustom made or commercially available processor, a central processingunit (CPU), an auxiliary processor among several processors associatedwith the mobile device 300, a semiconductor-based microprocessor (in theform of a microchip or chip set), or generally any device for executingsoftware instructions. When the access point 14 is in operation, theprocessor 102 is configured to execute software stored within memory orthe data store 108, to communicate data to and from the memory or thedata store 108, and to generally control operations of the access point14 pursuant to the software instructions. In an exemplary embodiment,the processor 102 may include a mobile-optimized processor such asoptimized for power consumption and mobile applications.

The radios 104 enable wireless communication in the distributed Wi-Fisystem 10. The radios 104 can operate according to the IEEE 802.11standard. The radios 104 include address, control, and/or dataconnections to enable appropriate communications on the distributedWi-Fi system 10. As described herein, the access point 14 includes aplurality of radios to support different links, i.e., backhaul links andclient links. The optimization 70 determines the configuration of theradios 104 such as bandwidth, channels, topology, etc. In an exemplaryembodiment, the access points 14 support dual band operationsimultaneously operating 2.4 GHz and 5 GHz 2×2 MIMO 802.11b/g/n/acradios having operating bandwidths of 20/40 MHz for 2.4 GHz and 20/40/80MHz for 5 GHz. For example, the access points 14 can support IEEE802.11AC1200 gigabit Wi-Fi (300+867 Mbps).

The local interface 106 is configured for local communication to theaccess point 14 and can be either a wired connection or wirelessconnection such as Bluetooth or the like. Since the access points 14 areconfigured via the cloud 12, an onboarding process is required to firstestablish connectivity for a newly turned on access point 14. In anexemplary embodiment, the access points 14 can also include the localinterface 106 allowing connectivity to the user device 22 (or a Wi-Ficlient device 16) for onboarding to the distributed Wi-Fi system 10 suchas through an app on the user device 22. The data store 108 is used tostore data. The data store 108 may include any of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,and the like)), nonvolatile memory elements (e.g., ROM, hard drive,tape, CDROM, and the like), and combinations thereof. Moreover, the datastore 108 may incorporate electronic, magnetic, optical, and/or othertypes of storage media.

The network interface 110 provides wired connectivity to the accesspoint 14. The network interface 104 may be used to enable the accesspoint 14 communicate to the modem/router 18. Also, the network interface104 can be used to provide local connectivity to a Wi-Fi client device16 or user device 22. For example, wiring in a device to an access point14 can provide network access to a device which does not support Wi-Fi.In an exemplary embodiment, all of the access points 14 in thedistributed Wi-Fi system 10 include the network interface 110. Inanother exemplary embodiment, select access points 14 which connect tothe modem/router 18 or require local wired connections have the networkinterface 110. The network interface 110 may include, for example, anEthernet card or adapter (e.g., 10BaseT, Fast Ethernet, GigabitEthernet, 10GbE). The network interface 110 may include address,control, and/or data connections to enable appropriate communications onthe network.

The processor 102 and the data store 108 can include software and/orfirmware which essentially controls the operation of the access point14, data gathering and measurement control, data management, memorymanagement, and communication and control interfaces with the server 20via the cloud. The processor 102 and the data store 108 may beconfigured to implement the various processes, algorithms, methods,techniques, etc. described herein.

Cloud Server and User Device

Referring to FIG. 6, in an exemplary embodiment, a block diagramillustrates functional components of the server 20, the Wi-Fi clientdevice 16, or the user device 22 which may be used with the distributedWi-Fi system 10. FIG. 6 illustrates functional components which can formany of the Wi-Fi client device 16, the server 20, the user device 22, orany general processing device. The server 20 may be a digital computerthat, in terms of hardware architecture, generally includes a processor202, input/output (I/O) interfaces 204, a network interface 206, a datastore 208, and memory 210. It should be appreciated by those of ordinaryskill in the art that FIG. 6 depicts the server 20 in an oversimplifiedmanner, and a practical embodiment may include additional components andsuitably configured processing logic to support features describedherein or known or conventional operating features that are notdescribed in detail herein.

The components (202, 204, 206, 208, and 210) are communicatively coupledvia a local interface 212. The local interface 212 may be, for example,but not limited to, one or more buses or other wired or wirelessconnections, as is known in the art. The local interface 212 may haveadditional elements, which are omitted for simplicity, such ascontrollers, buffers (caches), drivers, repeaters, and receivers, amongmany others, to enable communications. Further, the local interface 212may include address, control, and/or data connections to enableappropriate communications among the aforementioned components.

The processor 202 is a hardware device for executing softwareinstructions. The processor 202 may be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the server 20, asemiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. Whenthe server 20 is in operation, the processor 202 is configured toexecute software stored within the memory 210, to communicate data toand from the memory 210, and to generally control operations of theserver 20 pursuant to the software instructions. The I/O interfaces 204may be used to receive user input from and/or for providing systemoutput to one or more devices or components. User input may be providedvia, for example, a keyboard, touchpad, and/or a mouse. System outputmay be provided via a display device and a printer (not shown). I/Ointerfaces 204 may include, for example, a serial port, a parallel port,a small computer system interface (SCSI), a serial ATA (SATA), a fibrechannel, Infiniband, iSCSI, a PCI Express interface (PCI-x), an infrared(IR) interface, a radio frequency (RF) interface, and/or a universalserial bus (USB) interface.

The network interface 206 may be used to enable the server 20 tocommunicate on a network, such as the cloud 12. The network interface206 may include, for example, an Ethernet card or adapter (e.g.,10BaseT, Fast Ethernet, Gigabit Ethernet, 10GbE) or a wireless localarea network (WLAN) card or adapter (e.g., 802.11a/b/g/n/ac). Thenetwork interface 206 may include address, control, and/or dataconnections to enable appropriate communications on the network. A datastore 208 may be used to store data. The data store 208 may include anyof volatile memory elements (e.g., random access memory (RAM, such asDRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g.,ROM, hard drive, tape, CDROM, and the like), and combinations thereof.Moreover, the data store 208 may incorporate electronic, magnetic,optical, and/or other types of storage media. In one example, the datastore 208 may be located internal to the server 20 such as, for example,an internal hard drive connected to the local interface 212 in theserver 20. Additionally, in another embodiment, the data store 208 maybe located external to the server 20 such as, for example, an externalhard drive connected to the I/O interfaces 204 (e.g., SCSI or USBconnection). In a further embodiment, the data store 208 may beconnected to the server 20 through a network, such as, for example, anetwork attached file server.

The memory 210 may include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, tape, CDROM, etc.), andcombinations thereof. Moreover, the memory 210 may incorporateelectronic, magnetic, optical, and/or other types of storage media. Notethat the memory 210 may have a distributed architecture, where variouscomponents are situated remotely from one another but can be accessed bythe processor 202. The software in memory 210 may include one or moresoftware programs, each of which includes an ordered listing ofexecutable instructions for implementing logical functions. The softwarein the memory 210 includes a suitable operating system (O/S) 214 and oneor more programs 216. The operating system 214 essentially controls theexecution of other computer programs, such as the one or more programs216, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices. The one or more programs 216 may be configured to implementthe various processes, algorithms, methods, techniques, etc. describedherein, such as related to the optimization 70.

Data Gathering

Again, in various exemplary embodiments, the systems and methods providetechniques for gathering data and measurements in the distributed Wi-Fisystem 10 in the configuration and optimization process 50 for use inthe optimization 70. As described herein, the measurements and gathereddata from the access points 14 can be referred to as measurements ordata collectively. This data can include, without limitation, trafficfrom/to different clients, signal strengths and data rates used from/todifferent clients, Wi-Fi channel conditions, performance experienced bydifferent traffic flows, congestion levels on the access points 14, etc.This data is reported to the cloud 12 to the servers 20 bothperiodically and in an event-driven fashion. The event-driven reportingcan be based on crossing an absolute or relative threshold. Further, thereporting frequency can be dynamically adjusted based on thresholds orlevels of the parameters being measured. The access points 14 can reportthe data as raw data, or when the volume of data is large, it can becompressed and reported through statistical measures such as the mean,median, distribution, etc. An objective of the data gathering is toperform the gathering and reporting while minimizing the impact on theperformance of active traffic flows that are being carried by the accesspoints 14.

Referring to FIG. 7, in an exemplary embodiment, a flowchart illustratesa data gathering process 300 by an access point 14 in the distributedWi-Fi system 10. The data gathering process 300 includes obtainingon-channel scanning data while operating on a home channel in thedistributed Wi-Fi system 10 (step 301); periodically or based on commandfrom a cloud-based system switching off the home channel and obtainingoff-channel scanning data for one or more off-channels (step 302); andproviding measurement data based on the on-channel scanning data and theoff-channel scanning to the cloud-based system for use in theoptimization of the distributed Wi-Fi system 10 (step 303). As describedherein, the home channel is a channel the associated radio 104 ispresently operating on based on a current configuration (such as basedon a previous optimization 70 or an initial configuration). The homechannel can also be for client links or backhaul links. That is, theaccess point 14 can be operating on a home channel for communicationwith the clients 16 on a client link as well as on another home channelfor communication with other access points 14 in the distributed Wi-Fisystem 10, i.e., backhaul links. The cloud-based system can be theservers 20 in the cloud 12.

The data gathering process 300 can further include processing theon-channel scanning data and the off-channel scanning data to determinethe measurement data (step 304). This processing is performed locally tothe access point 14 to determine measurements which are then transmittedto the server 20. In another exemplary embodiment, the access point 14can merely transmit the on-channel scanning data and the off-channelscanning data as raw data without processing. However, the processingreduces bandwidth requirements over the distributed Wi-Fi system 10 forthe data gathering process 300. Also, the processing can simply includecompressing the on-channel scanning data and the off-channel scanningdata prior to transmission to the server 20. The processing can includedetermining a division of time that the home channel is divided based ona combination of direct measurements and computations based on thedirect measurements. The processing can include determining delays ofpackets over the distributed Wi-Fi system through one of directmeasurements and statistics of delay at the access point. Finally, thedata gathering process 300 can further include switching back to thehome channel subsequent to the obtaining off-channel scanning data (step305).

For illustration purposes, the data gathering process 300 is describedwith reference to the distributed Wi-Fi system 10. Those skilled in theart will recognize the data gathering process 300 can also operate withother type of Wi-Fi systems such as the Wi-Fi mesh network 32, the Wi-Firepeater network 33, etc. That is, the data gathering process 300generally contemplates operation with any multiple access point 14, 34system and can be implemented by the mesh nodes 36, the access point 34,the repeater 38, etc. Again, the data gathering process 300 isadvantageous for cloud-controlled Wi-Fi systems to able intelligent,remote control and optimization.

In an exemplary embodiment, the scanning for either on-channel scanningdata or off-channel scanning data can be performed and reported atdifferent channel bandwidths (e.g., 20, 40, 80. 160 MHz). For example,the on-channel scanning can be performed at a different channelbandwidth than what is currently configured on the home channel.Further, the off-channel scanning can be set at any of the differentchannel bandwidths. For example, the periodic scanning can rotatebetween the different channel bandwidths to provide the cloud-basedsystem more representative details on how the Wi-Fi system is operating.

The measurement data can include a plurality of Received Signal StrengthIndicators (RSSI), achievable data rates, capacity, load, error rates,delays, and fractions of time spent transmitting and receiving. Theswitching off can be determined based on a load of the access point 14.For example, the process 300 can include monitoring a status of queuesin the access point 14 to determine when the load is low for determiningthe switching off. The process 300 can further include notifying otheraccess points in the distributed Wi-Fi system 10 of the switching offvia one of a broadcast frame and an information element in the beacon,with an offset from a Timing Synchronization Function (TSF) timer.Optionally, the access point 14 is lightly loaded compared to at leastone additional access point 14 in the distributed Wi-Fi system 10, andwherein the access point 14 is configured to perform the obtainingoff-channel scanning data for the at least one additional access point14. The access point 14 for the off-channel scanning data can send proberequests for a particular Service Set Identifier (SSID) to measuresignal strengths to a particular neighbor to reduce the number of proberesponses received. The access point 14 for the off-channel scanningdata can send frames that spoof another in-network Basic Service Set(BSS) to elicit responses from neighboring access points and clients.The process 300 can further include receiving configuration data fromthe cloud-based system based on the optimization, wherein the providingmeasurement data is performed over a statistics channel, and thereceiving configuration data is performed over a configuration channeldifferent from the statistics channel.

In another exemplary embodiment, an access point 14 in the distributedWi-Fi system 10 includes a plurality of radios communicating on thedistributed Wi-Fi system; and a processor communicatively coupled to theplurality of radios and configured to cause one or more radios of theplurality of radios to obtain on-channel scanning data while operatingon a home channel in the distributed Wi-Fi system; periodically or basedon command from a cloud-based system cause the one or more radios toswitch off the home channel and obtain off-channel scanning data for oneor more off-channels; and providing measurement data based on theon-channel scanning data and the off-channel scanning data to thecloud-based system for use in the optimization of the distributed Wi-Fisystem 10.

In a further exemplary embodiment, the distributed Wi-Fi system 10includes a plurality of access points 14 communicatively coupled to oneanother and at least one access point communicatively coupled to agateway providing external communication for the distributed Wi-Fisystem 10 and communication to a cloud-based system; wherein each of theplurality of access points 14 are configured to obtain on-channelscanning data while operating on a home channel in the distributed Wi-Fisystem; periodically or based on command from a cloud-based systemswitch off the home channel and obtain off-channel scanning data for oneor more off-channels; and provide measurement data based on theon-channel scanning data and the off-channel scanning data to thecloud-based system for use in the optimization of the distributed Wi-Fisystem 10.

In yet another exemplary embodiment, a method of gathering data by theaccess point 14, 34 or mesh node 36 or repeater 38 in a Wi-Fi systemincludes periodically or based on command from a cloud-based systemperforming one or more of i) obtaining on-channel scanning data whileoperating on a home channel and ii) switching off the home channel andobtaining off-channel scanning data for one or more off-channels; andproviding measurement data based on one or more of the on-channelscanning data and the off-channel scanning data to the cloud-basedsystem for use in the optimization of the Wi-Fi system, wherein themeasurement data comprises one or more of raw data and processed data.

In yet another exemplary embodiment, an access point 14, 34, mesh node36 or repeater 38 configured to gather data for optimization includes aplurality of radios communicating on the Wi-Fi system; and a processorcommunicatively coupled to the plurality of radios and configured to:periodically or based on command from a cloud-based system cause the oneor more radios to one or more of i) obtain on-channel scanning datawhile operating on a home channel and ii) switch off the home channeland obtain off-channel scanning data for one or more off-channels; andprovide measurement data based on the on-channel scanning data and theoff-channel scanning data to the cloud-based system for use in theoptimization of the distributed Wi-Fi system, wherein the measurementdata comprises one or more of raw data and processed data.

In yet another exemplary embodiment, a Wi-Fi system configured to gatherdata for optimization includes a plurality of access pointscommunicatively coupled to one another and at least one access pointcommunicatively coupled to a gateway providing external communicationfor the Wi-Fi system and communication to a cloud-based system; whereineach of the plurality of access points are configured to: periodicallyor based on command from a cloud-based system perform one or more of i)obtain on-channel scanning data while operating on a home channel andii) switch off the home channel and obtain off-channel scanning data forone or more off-channels; and provide measurement data based on theon-channel scanning data and the off-channel scanning data to thecloud-based system for use in the optimization of the Wi-Fi system,wherein the measurement data comprises one or more of raw data andprocessed data.

The access points 14 can periodically or upon receiving a command fromthe cloud 12 perform off-channel scans with the radio(s) 104 by brieflyswitching and monitoring a channel other than its home channel and atdifferent channel widths (e. g. 20, 40, 80, and 160 MHz). During thescanning period, the access point 14 can collect information on thebusyness of the channel both due to Wi-Fi and non-Wi-Fi transmissionsand identify the occupancy levels due to each of the neighboring BasicService Sets (BSSs). The access point 14 can also measure the signalstrengths from neighboring access points 14 and non-associated clients16 by receiving management frames such as beacons as well as dataframes. In order to increase the efficiency of data collection fromin-network access points 14 (ones that are part of the distributed Wi-Fisystem 10) and clients 16, the access point 14 performing the scan cansend frames that spoof another in-network BSS to elicit responses fromneighboring access points 14 and clients 16. Alternatively, theneighboring in-network access points 14 can be instructed to transmit totheir associated clients 16 eliciting responses that can, in turn, bemeasured by the scanning access point 14. The access points 14 can alsomeasure the Modulation and Coding Scheme (MCSs) being used betweenneighboring access points 14 and their clients 16.

To optimize the distributed Wi-Fi system 10, it is beneficial to knowthe signal strengths from all clients 16 to all access points 14, eachof which is a potential place for the clients 16 to associate with.However, some access points 14 have trouble receiving and recording thesignal strength of clients 16 that are not associated with them. One wayto circumvent this problem is to put the access point 14 doing the scan(whether off-channel or on-channel), into a multiple BSSID mode, and addthe neighboring access points 14 BSSIDs to the access point list. Nextthe access point 14 should be configured not to send acknowledgments.This pair of actions will allow the access point 14 to silently listento all the client transmissions, even transmissions from clients 16 thatare not associated with that access point 14. In an exemplaryembodiment, a transmission can be triggered from a Wi-Fi client device16 from which measurement is desired by the measuring access point 14transmitting a packet to it from the access point 14 that the Wi-Ficlient device 16 is already connected to, i.e., coordination betweenaccess points 14 to trigger the Wi-Fi client device 16 to transmit whicha different access point 14 is listening to that Wi-Fi client device 16.

While performing off-channel scans, the access point 14 is unable totransmit and receive on its home channel which could potentially disruptthe performance of traffic flows. To mitigate disruption, the accesspoint 14 could perform scans only when they are idle or lightly loaded.Again, this can be based on monitoring a queue associated with theaccess point 14. Indeed, the multiple access points 14 in a home couldbe leveraged to infer the interference at heavily loaded nodes fromproximate idle or lightly loaded access points 14 whose measurements arehighly correlated. In that case, the heavily loaded access point 14would never need to go off-channel to scan, as surrounding lightlyloaded access points 14 would do the off-channel scanning for it.Further, access points 14 could decide to skip or postpone scans basedon estimating the probability of packet loss using information on thefree memory available to buffer data that arrives during the scanningperiod. The access points 14 can also notify other in-network accesspoints 14 of their planned absence using a broadcast frame or aninformation element in the beacon, with an offset from a TimingSynchronization Function (TSF) timer. This ensures that other in-networkaccess points 14 will not attempt to transmit to the access point 14when it is not on its home channel. Associated client devices 16 couldeven be forced to connect to a different access point 14 in the vicinityfor the duration of the off-channel scan and transitioned back to theoriginal access point 14 after the scan is complete. In order to reducethe amount of time the access point 14 is away from its home channel, itcould scan channels sequentially, returning to its home channel betweenscans. The access points 14 could also perform directed scans by sendingprobe requests for a particular SSID to measure signal strengths to aparticular neighbor. This reduces the amount of time the access pointneeds to spend off channel, but triggering a response quickly from theneighbor the access point is trying to learn about. In addition, sendingprobe requests to a particular SSID reduces number of probe responsesreceived, thus reducing the overhead of transmissions and thereforeinterference on the channel generated by gathering the data. Further,the access point 14, prior to switching off-channel, can move anyassociated clients 16 to other access points 14. Also, this could bedone by the cloud-based system. With no associated clients 16, theaccess point 14 can perform off-channel scanning without networkdisruption.

While an access point 14 is on the home channel transmitting andreceiving to associated clients 16 (or on the backhaul link transmittingand receiving to other access points 14), it measures the traffictransmitted to and received from each client 16 as well as the MCS ratesused, the number of Media Access Control (MAC) Protocol Data Unit(MPDUs) in each aggregate, the packet error rate, the number of missingacknowledgements, etc. While on the home channel, the access point 14can also listen to transmissions on that same channel that areassociated with a different access point 14 operating on that samechannel. While on the home channel, operating normally and serving anyassociated Wi-Fi client devices 16, the access point 16 can also begathering measurements on all devices operating on that same channelthat is within radio range. This includes Wi-Fi client devices 16 aswell as other access points 14.

Another important piece of data to gather is the delay of packetstraversing the network. These delays could be derived from directmeasurements, time stamping packets as they arrive into the distributedWi-Fi system 10 at the gateway (modem/router 18), and measuring theelapsed time as they depart at the final access point 14. However, suchmeasurement requires some degree of time synchronization between theaccess points 14. Another approach would be to measure the statistics ofdelay going through each access point 14 individually. The average totaldelay through the distributed Wi-Fi system 10, and the distribution ofthe delays given some assumptions could then be calculated based on thedelay statistics through each access point 14 individually. Delay canthen become a parameter to be minimized in the optimization 70. Thesedelays should be obtained or derived from the pathway to each client 16in the distributed Wi-Fi system 10.

The access point 14 measures the occupancy of the transmit queue,measuring the queue length, i.e., the backlog in the transmit queuesover time as well as the queue utilization, i.e., the fraction of thetime that the queue is non-empty. These measures are well correlatedwith the performance experienced by the traffic flows carried by theaccess point 14. The maximum throughput that can be achieved by theaccess point 14 if the traffic it carried were to be scaled up can bedetermined as the ratio of the access point's 14 throughput to the queueutilization.

Referring to FIG. 8, in an exemplary embodiment, a graph illustratesfractions of time-related to a channel, such as the home channel. On itshome channel, the access point 14 can also measure/infer the fractionsof time that the channel is busy due to the activity of Wi-Fitransmitters in neighboring networks, i.e., interference (Busy %); theaccess point 14 spends transmitting (Transmit %); the access point 14spends receiving from associated clients 16 (Receive %); the accesspoint 14 spends counting down its contention window when the channel isidle, and the node has packets in its transmit queue (Backoff %); thechannel is idle and the access point 14 has an empty transmit queue(Idle %). FIG. 8 illustrates an example of the channel. Understandinghow time is distributed across these categories is very useful. Theratio of the access point's 14 throughput to its transmit time fractioncorresponds to the effective data rate the access point 14 achieves whenit gains access to the channel. The Idle % corresponds to the time thatthe access point 14 could hypothetically use to transmit data, and is areflection of the excess capacity available to the access point 14.However, not all these time fractions may be directly measurable at theaccess point 14 due to implementation limitations. The transmit %,receive %, and busy % may be directly reported by a driver, but thebackoff % and idle % cannot be distinguished. Jointly measuring thequeue occupancy and channel time fractions over a period of time allowsthe different time fractions to be separately estimated by leveragingthe relationship between the two, see FIG. 8. For instance, the backoff% can be computed as Queue utilization*(100−Receive %−Busy %)−Transmit%.

It is also useful to distinguish the interference due to the accesspoint 14 within the distributed Wi-Fi system 10 that can be centrallycontrolled from the interference due to nodes outside the distributedWi-Fi system 10. Data jointly gathered from access points 14 over a timeperiod can be used to estimate the in-network interference using thetransmit and receive activity reported by neighboring nodes as well asthe signal strength between nodes. Thus, the out of network interferencecan be estimated by subtracting out the in-network interference from thetotal measured interference at the access point 14. An example of such acalculation would be to measure the total busy time at one access point14 in the home. The other access points 14 in the distributed Wi-Fisystem 10 in the home could report the time they spent transmitting. Thetime that all other access points 14 in the home's distributed Wi-Fisystem 10 spend transmitting could be subtracted from the busy time atthe access point 14 of interest. This would remove all the trafficassociated with other access points 14 that are part of the home'sdistributed Wi-Fi system 10, leaving only the airtime consumed bytransmissions from networks in neighboring homes. For the above processto be successful, the channel time fractions must be measured orestimated over an identical time period for all access points 14. Onetechnique that could be employed is the synchronization of datareporting across all access points 14 in the distributed Wi-Fi system 10so that all access points 14 report statistics for identical timeslices. Another technique that could be employed is to aggregate dataover a sufficient period of time from all access points 14 and computestatistical measures of the channel time fractions over this period touse for the estimation. This alleviates the need to synchronize the datagathering across the access points 14.

The cloud-based system, i.e., the server 20, is generally configured touse the measurement data for optimization and control of the distributedWi-Fi system 10. In an exemplary embodiment, the server 20 can beconfigured to perform analysis of the measurement data from multipleaccess points 14 at once to derive other measurement data. For example,this can include subtracting the TX time of an access point 14 with theRX time of another access point 14. Here, the measurement data frommultiple access points 14 can be time synchronized allowing alignment inthe cloud 12 for comparison, analysis, etc. Alternatively, themeasurement data from multiple access points 14 can be only coarselytime synchronized such as through averaging over time periods which aremuch longer than time synchronization uncertainty, and subtracting orotherwise deriving a result from measurements at different access points14 by working with the values averaged over time.

In order to reduce the volume of data that is collected from the accesspoints 14, redundant information can be omitted, e.g., data oncommunication between a pair of access points 14 need only be reportedby one of them, say the transmitter. Data can also be compressed bysending statistics such as averages, moving averages, histograms, etc.instead of raw data. When raw data is sent, reports can be batchedacross a long-time period and reported in a compressed format. Thevolume of data can be further reduced by reducing the frequency ofperiodic reports and supplementing them with event driven reporting. Thecloud 12 could configure nodes with parameters such as absolutethresholds for different quantities (load, capacity, Received SignalStrength Indicator (RSSI), etc.), and the access points 14 could reportdata only when the thresholds are crossed. Relative thresholds can alsobe used with access points 14 sending reports only when the new reportdiffers from the one previously sent by at least the specified margin.Alternatively, the thresholds could be used to specify policies thatmodulate the frequency at which access points 14 gathers and reportdata. Some measurements that are intrusive or otherwise cumbersome tocalculate (e.g., off-channel scans) could be triggered only when adifferent parameter crosses a specified threshold. Additionally, somestatistics could be measured and reported only when explicitly requestedby the cloud 12. In this way, any costs due to overheads imposed by datameasurement and reporting can be controlled.

In an exemplary embodiment, the access points 14 can continually takemeasurements, such as every minute or some other configurable timeperiod, but the reporting to the cloud-based system can be done inbatches. For example, obtain measurements over X time periods and reportthe X sets of measurements at once. This approach reduces the frequencyof communication to the cloud 12, while still ensuring the cloud-basedsystem gets all of the necessary measurements for optimization. Further,the access point 14 can be configured to compress the measurement dataprior to sending to the cloud 12 to reduce the overall bandwidthrequired. In another exemplary embodiment, to further reduce reportingbandwidth, there can be coordination between access points 14 on what isreported such as to reduce redundancy. For example, it is only necessaryto report TX statistics on a given link and not necessary to give thecorresponding RX statistics from the partner node on the link since theTX and RX statistics for a given link will be the same and provide thesame information.

Separation of Statistics and Configuration-Channel

Referring to FIG. 9, in an exemplary embodiment, a network diagramillustrates a network 400 with an access point 14 in the distributedWi-Fi system 10 communicating with a configuration service 402 and astatistics service 404 in the cloud 12. The data gathered by the accesspoints 14 in the distributed Wi-Fi system 10 as described above needs tobe moved into the cloud 12. It is beneficial to do this over a separatechannel from the channel used to configure and control the access points14. This is because managing the access points 14 from the cloud 12 hasdistinct traffic patterns from collecting data. The configuration ofaccess points 14 is infrequent and must be reliable and transactional,whereas statistics reporting from the access points 14 to the services402, 404 has large volumes hence must be efficient and can accommodatesome loss of data. Hence, the access points 14 can implement differentcommunication channels and approaches for configuration and statistics,namely a configuration channel 406 to the configuration service 402 anda statistics channel 408 to the statistics service 404.

The configuration service 402 and the statistics service 404 aredeployed in the cloud 12, such as on the same or different servers 20.The services 402, 404 can be referred to as a cloud-based system whichcan operate on the servers 20 in the cloud 12. These services 402, 404can communicate via different protocols as well as be at differentlocations. Each of the access points 14 in the distributed Wi-Fi system10 are configured to communicate with both of the services 402, 404,over the configuration channel 406 and the statistics channel 408,respectively. The configuration-channel 406 can use a transactionoriented reliable communication protocol such as Open vSwitch DatabaseManagement Protocol (OVSDB) to interact with the configuration service402. The statistics channel 408 can use an efficient and lightweightcommunication protocol such as ProtoBuf over Message Queue TelemetryTransport (MQTT) to report statistics (measurement data) to thestatistics service 404.

This approach provides several advantages. The configuration of accesspoints 14 is critical activity and does not get impacted due tosaturation of traffic or high volume related problems in statisticschannel 408. The statistics communication protocol can be selected forhigh efficiency thereby minimizing management overhead of the accesspoints 14.

It will be appreciated that some exemplary embodiments described hereinmay include one or more generic or specialized processors (“one or moreprocessors”) such as microprocessors; Central Processing Units (CPUs);Digital Signal Processors (DSPs): customized processors such as NetworkProcessors (NPs) or Network Processing Units (NPUs), Graphics ProcessingUnits (GPUs), or the like; Field Programmable Gate Arrays (FPGAs); andthe like along with unique stored program instructions (including bothsoftware and firmware) for control thereof to implement, in conjunctionwith certain non-processor circuits, some, most, or all of the functionsof the methods and/or systems described herein. Alternatively, some orall functions may be implemented by a state machine that has no storedprogram instructions, or in one or more Application Specific IntegratedCircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic or circuitry. Ofcourse, a combination of the aforementioned approaches may be used. Forsome of the exemplary embodiments described herein, a correspondingdevice in hardware and optionally with software, firmware, and acombination thereof can be referred to as “circuitry configured oradapted to,” “logic configured or adapted to,” etc. perform a set ofoperations, steps, methods, processes, algorithms, functions,techniques, etc. on digital and/or analog signals as described hereinfor the various exemplary embodiments.

Moreover, some exemplary embodiments may include a non-transitorycomputer-readable storage medium having computer readable code storedthereon for programming a computer, server, appliance, device,processor, circuit, etc. each of which may include a processor toperform functions as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, an optical storage device, a magnetic storage device, a ROM(Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM(Erasable Programmable Read Only Memory), an EEPROM (ElectricallyErasable Programmable Read Only Memory), Flash memory, and the like.When stored in the non-transitory computer readable medium, software caninclude instructions executable by a processor or device (e.g., any typeof programmable circuitry or logic) that, in response to such execution,cause a processor or the device to perform a set of operations, steps,methods, processes, algorithms, functions, techniques, etc. as describedherein for the various exemplary embodiments.

Although the present disclosure has been illustrated and describedherein with reference to preferred embodiments and specific examplesthereof, it will be readily apparent to those of ordinary skill in theart that other embodiments and examples may perform similar functionsand/or achieve like results. All such equivalent embodiments andexamples are within the spirit and scope of the present disclosure, arecontemplated thereby, and are intended to be covered by the followingclaims.

What is claimed is:
 1. A method for gathering data by an access point ina Wi-Fi system for optimization, the method comprising: periodically orbased on command from a cloud-based system performing one or more of i)obtaining on-channel scanning data while operating on a home channel andii) switching off the home channel and obtaining off-channel scanningdata for one or more off-channels; and providing measurement data basedon one or more of the on-channel scanning data and the off-channelscanning data to the cloud-based system for use in the optimization ofthe Wi-Fi system, wherein the measurement data comprises one or more ofraw data and processed data.
 2. The method of claim 1, wherein, when themeasurement data is the processed data, the method further comprisesdetermining a division of time that the home channel is divided based ona combination of direct measurements and computations based on thedirect measurements.
 3. The method of claim 1, wherein, when themeasurement data is the processed data, the method further comprisesdetermining delays of packets over the Wi-Fi system through one ofdirect measurements and statistics of delay at the access point.
 4. Themethod of claim 1, wherein the measurement data comprises a plurality ofReceived Signal Strength Indicators (RSSI), achievable data rates,capacity, load, error rates, delays, interference, and fractions of timespent transmitting and receiving.
 5. The method of claim 1, wherein theswitching off is determined based on a load of the access point.
 6. Themethod of claim 1, further comprising: notifying other access points inthe Wi-Fi system of the switching off.
 7. The method of claim 1, whereinthe access point is lightly loaded compared to at least one additionalaccess point in the Wi-Fi system, and wherein the access point isconfigured to perform the obtaining off-channel scanning data for the atleast one additional access point.
 8. The method of claim 1, wherein theaccess point for the off-channel scanning data sends probe requests fora particular Service Set Identifier (SSID) to measure signal strengthsto a particular neighbor to reduce a number of probe responses received.9. The method of claim 1, wherein the access point for the off-channelscanning data sends frames that spoof another in-network Basic ServiceSet Identifier (BSSID) to elicit responses from neighboring accesspoints and clients.
 10. The method of claim 1, further comprising:receiving configuration data from the cloud-based system based on theoptimization, wherein the providing measurement data is performed over astatistics channel and the receiving configuration data is performedover a configuration channel different from the statistics channel. 11.The method of claim 1, wherein the one or more of the on-channelscanning data and the off-channel scanning data is obtained at differentchannel bandwidths.
 12. The method of claim 1, further comprising:causing Wi-Fi client devices to move to other access points prior to theswitching off.
 13. An access point in a Wi-Fi system configured togather data for optimization, the access point comprising: a pluralityof radios communicating on the Wi-Fi system; and a processorcommunicatively coupled to the plurality of radios and configured to:periodically or based on command from a cloud-based system cause the oneor more radios to one or more of i) obtain on-channel scanning datawhile operating on a home channel and ii) switch off the home channeland obtain off-channel scanning data for one or more off-channels; andprovide measurement data based on the on-channel scanning data and theoff-channel scanning data to the cloud-based system for use in theoptimization of the distributed Wi-Fi system, wherein the measurementdata comprises one or more of raw data and processed data.
 14. Theaccess point of claim 13, wherein the processor is further configuredto: notify other access points in the Wi-Fi system of the switching off.15. The access point of claim 13, wherein the access point is lightlyloaded compared to at least one additional access point in the Wi-Fisystem, and wherein the access point is configured to obtain theoff-channel scanning data for the at least one additional access point.16. The access point of claim 13, wherein the access point for theoff-channel scanning data sends probe requests for a particular ServiceSet Identifier (SSID) to measure signal strengths to a particularneighbor to reduce a number of probe responses received.
 17. The accesspoint of claim 13, wherein the access point for the off-channel scanningdata sends frames that spoof another in-network Basic Service SetIdentifier (BSSID) to elicit responses from neighboring access pointsand clients.
 18. The access point of claim 13, wherein the processor isfurther configured to: receive configuration data from the cloud-basedsystem based on the optimization, wherein the measurement data isprovided over a statistics channel and the receiving configuration datais provided over a configuration channel different from the statisticschannel.
 19. The access point of claim 13, wherein the processor isfurther configured to: cause Wi-Fi client devices to move to otheraccess points prior to the switching off.
 20. A cloud-based systemconfigured to obtain data from a Wi-Fi system for optimization, thecloud-based system comprising: a network interface communicativelycoupled to the Wi-Fi system comprising a plurality of access pointscommunicatively coupled to one another and at least one access pointcommunicatively coupled to a gateway providing external communication tothe cloud-based system; one or more processors communicatively coupledto the network interface; and memory storing instructions that, whenexecuted, cause the one or more processors to: receive periodically orbased on command from the cloud-based system one or more of i)on-channel scanning data while an access point is operating on a homechannel and ii) off-channel scanning data for one or more off-channelswhere the access point switches off the home channel; and analyzemeasurement data based on the on-channel scanning data and theoff-channel scanning data for use in the optimization of the Wi-Fisystem, wherein the measurement data comprises one or more of raw dataand processed data, and wherein the measurement data is analyzed fromthe plurality of access points.